USING PYTHON

1)Use logistic regression to create a predictive model:

Use 70% of data for tarining and consider 30% of data for testing. (Use random_state=0)

Use all features to create your predictive model

"target" is your dependent variable. It shows whether the subject suffers form a heart disease or not.

2)Use similar structure as above to create a naive bayes predictive model.

3) Use in-sample data (train data) and out-of-sample data (test data) to check the accuracy of your naive bayes model: What is the general expectation? should we expect to get higher accuray on in-sample data or out-of-sample data What is the result in your case?

4)Find precision, recall and F1 scores for both logistic regression and naive bayes models using out-of-sample data (test data) and explain which one works better than the other?

Sorry I couldn't attach excel file. Could you please use the data in the pics below to solve questions

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